Data Science

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The Profession

Data scientists and data analysts are being sought across a wide range of industries in today’s business world. Understanding the latest data science methods allows you to visualize data, leverage models, and derive relevant key insights.

Data is a major commodity that is increasingly in high demand in industries from mining to health care. Developing skills in the areas of data science and/or data analytics enables you to leverage your strengths in quantitative analysis to extrapolate meaningful insights.

Become well-versed in core competencies from applied statistics to data visualization, or focus on data-driven decision-making and management. With these skills, you’ll be equipped to improve products, customer service, marketing, and strategies with quality data insights. Embark on a new career, add an additional skillset, or pursue designations.

Specializing in Data Science and Machine Learning requires a strong quantitative background and essential programming skills. Those with a commerce or business background are well-suited to specialize in Data Analytics for Business.

$95,350

Average Data Scientist Salary in Canada (Indeed)

42%

of large employers suffer from skill shortages in Big Data/Analytics (KPMG)

This course aims to provide an overview of how data science can help drive business decisions and create new business models. The emphasis is placed on data strategy and how to move from data to insight. The course explores the data science process and how companies could surmount the different challenges they
face when implementing a data driven business including ethics, data governance and privacy. The evolution of data technology and storage, as well as application of
data science tools and techniques to different business areas such as customer and web analytics, operations analytics, human resources related analytics are
explored through examples from various fields such as retail, healthcare and marketing.

This course familiarizes participants with different aspects of large data sets and how they are managed both on site and in the Cloud. Emphasis is placed on providing participants with hands-on experience from data ingestion to analysis of large data sets, both data-atrest or data-in-motion (streaming data), including defining Big Data and its 5 V's: Volume, Velocity, Variety, Veracity, and Value. Architectures of distributed databases and storage, ecosystems such as Hadoop and Spark are covered followed by introduction to Scala, Spark-Shell and PySpark.

This course aims to introduce participants to essential machine learning methods and techniques through an end-to-end machine learning project. Emphasis is placed on practical experience with machine learning using Python programming language, scikit-learn and TensorFlow, as well as on understanding classification and training models. The course will provide an introduction to artificial Neural Networks, deep learning, convolutional and recurrent neural nets and reinforcement learning.

This course provides an overview of fundamental statistical and mathematical concepts needed to perform statistical data analysis to support business decisionmaking and projections such as probability, random variables, descriptive statistics, regression modelling, common probability distributions, experimental design.

The objective of this course is to introduce fundamental analytical methods and tools used to collect, analyze and interpret business data. An overview of NoSQL databases, RDBMS databases and data structures is provided. Participants will be exposed to a complete data cycle using powerful tools such as Excel, SQL and Tableau to analyze data, create forecasts and models, design visualizations, and communicate insights.

This course focuses on the capabilities needed by organizations to successfully create a data driven culture and to properly lead data related projects all the way from idea to delivery. Topics covered include different roles and responsibilities within a data project, how to recruit, evaluate, and develop a team with diverse and complementary skill sets, define the goals of each stage of the data science pipeline. Challenges of data governance will also be addressed along with best practices in governance and compliance.

This capstone course supported by our industry partners will provide the opportunity to apply all the knowledge gained during the program in order to build a full data science pipeline from preparing and visualizing data, building and testing models, analyzing results and deriving business insights from their analysis. The focus is placed on communicating the insights gleaned from the data analysis through visualizations and on presenting the recommendations reached.

As machine learning continues to become more present in our everyday lives, it's important that the next generation of business and technical leaders are equipped with the necessary skills to take advantage of the possibilities that this new technology brings. I look forward to sharing my own insight and knowledge on the topic, and hope that graduates of this course will go on to apply their learnings for positive impact.

Corporate learning

The McGill School of Continuing Studies (SCS) offers professional development and educational opportunities for corporate clients and local and international partners. Whether you are a multinational corporation, international organization, small or medium-sized enterprise, government body or educational institution seeking specialized courses or workshops or a comprehensive program for your employees, SCS has the solution for you.